Legged robots are useful in tasks such as search and rescue because
they can effectively navigate on rugged terrain. However, it is
difficult to design controllers for them that would be stable and
robust. Learning the control behavior is difficult because optimal
behavior is not known, and the search space is too large for
reinforcement learning and for straightforward evolution. As a
solution, this paper proposes a modular approach for evolving neural
network controllers for such robots. The search space is
effectively reduced by exploiting symmetry in the robot morphology,
and encoding it into network modules. Experiments involving
physically realistic simulations of a quadruped robot produce the
same symmetric gaits, such as pronk, pace, bound and trot, that are
seen in quadruped animals. Moreover, the robot can transition
dynamically to more effective gaits when faced with obstacles. The
modular approach also scales well when the number of legs or their
degrees of freedom are increased. Evolved non-modular controllers,
in contrast, produce gaits resembling crippled animals that are much
less effective and do not scale up as a result. Hand-designed
controllers are also less effective, especially on an obstacle
terrain. These results suggest that the modular approach is
effective for designing robust locomotion controllers for
multilegged robots.